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Record W2024551212 · doi:10.1088/0004-637x/698/2/1893

GEOMETRICAL PROPERTIES OF AVALANCHES IN A PSEUDO-3D CORONAL LOOP

2009· article· en· W2024551212 on OpenAlexaff
Laura Morales, Paul Charbonneau

Bibliographic record

VenueThe Astrophysical Journal · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsCentre for Research in Astrophysics of QuébecUniversité de Montréal
Fundersnot available
KeywordsPhysicsFractalCoronal loopFractal dimensionSolar flareSuperposition principleComputational physicsStatistical physicsGeometryAstrophysicsMechanicsMagnetic fieldMathematical analysisSolar windCoronal mass ejection

Abstract

fetched live from OpenAlex

We investigate the geometrical properties of energy release of synthetic coronal loops constructed using a recently published self-organized critical avalanche model of solar flares. The model is based on an idealized representation of a coronal loop as a bundle of closely packed magnetic flux strands wrapping around one another in response to photospheric fluid motions, much as in Parker's nanoflare model. Simulations are performed with a two-dimensional cellular automaton that satisfies the constraint ∇ · B = 0 by design. We transform the avalanching nodes produced by simulations into synthetic flare images by converting the two-dimensional lattice into a bent cylindrical loop that is projected onto the plane of the sky. We study the statistical properties of avalanches peak snapshots and time-integrated avalanches occurring in these synthetic coronal loops. We find that the frequency distribution of avalanche peak areas A assumes a power-law form with an index αA ≃ 2.37, in excellent agreement with observationally inferred values and reducing error bars from previous works. We also measure the area fractal dimension D of avalanches produced by our simulations using the box counting method, which yields 1.17 ⩽ D ⩽ 1.24, a result falling nicely within the range of observational determinations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.220
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2009
Admission routes1
Has abstractyes

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